A Pre-restructured Learning-ISTA Deep Network for Millimeter Wave Antenna Array Diagnosis

Energy consumption, signal gain and spectral efficiency have become major concerns of 5G and millimeter wave, especially in the Internet of Things (IoT) scenario. Radiation pattern describes the dependence of the intensity and direction of a radio wave emitted by an antenna or other sources. The radiation pattern of the antenna array are easily affected by water molecules, dust, and the like in the air due to the densely packed antenna array in millimeter-wave system. The reflection and refraction of the antenna signal are caused by the bloakages, and the radiation pattern is changed. In this paper, a reduced model of the antenna diagnosis is built and the restructured iterative shrinkage-thresholding algorithm (ISTA-R) and restructured learning iterative shrinkage-thresholding algorithm (LISTA-R) are proposed to estimate the blocking coefficient and blocking position. The simulations show that the proposed algorithms can efficiently cut down the number of iterations and can improve the performance of real-time diagnosis.